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  <title>ObjectDetection(7)_YOLOV1 | 鲨鱼之家</title>
  <meta name="description" content="什么是YOLO 预测流程  边界框  如何理解上图中编码行为 边界框的置信度 边界框的任务   预测类别的条件概率 Class-specific confidence scores for each box   网络结构 Fast YOLO 训练  预训练  预训练使用的数据集 预训练时的网络结构 预训练时的输入   正式训练  训练时的网络结构 训练时的输入 最后一层   损失函数   模型">
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      ObjectDetection(7)_YOLOV1
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<ul>
<li><a href="#%E4%BB%80%E4%B9%88%E6%98%AFyolo">什么是YOLO</a></li>
<li><a href="#%E9%A2%84%E6%B5%8B%E6%B5%81%E7%A8%8B">预测流程</a>
<ul>
<li><a href="#%E8%BE%B9%E7%95%8C%E6%A1%86">边界框</a>
<ul>
<li><a href="#%E5%A6%82%E4%BD%95%E7%90%86%E8%A7%A3%E4%B8%8A%E5%9B%BE%E4%B8%AD%E7%BC%96%E7%A0%81%E8%A1%8C%E4%B8%BA">如何理解上图中编码行为</a></li>
<li><a href="#%E8%BE%B9%E7%95%8C%E6%A1%86%E7%9A%84%E7%BD%AE%E4%BF%A1%E5%BA%A6">边界框的置信度</a></li>
<li><a href="#%E8%BE%B9%E7%95%8C%E6%A1%86%E7%9A%84%E4%BB%BB%E5%8A%A1">边界框的任务</a></li>
</ul>
</li>
<li><a href="#%E9%A2%84%E6%B5%8B%E7%B1%BB%E5%88%AB%E7%9A%84%E6%9D%A1%E4%BB%B6%E6%A6%82%E7%8E%87">预测类别的条件概率</a></li>
<li><a href="#class-specific-confidence-scores-for-each-box">Class-specific confidence scores for each box</a></li>
</ul>
</li>
<li><a href="#%E7%BD%91%E7%BB%9C%E7%BB%93%E6%9E%84">网络结构</a></li>
<li><a href="#fast-yolo">Fast YOLO</a></li>
<li><a href="#%E8%AE%AD%E7%BB%83">训练</a>
<ul>
<li><a href="#%E9%A2%84%E8%AE%AD%E7%BB%83">预训练</a>
<ul>
<li><a href="#%E9%A2%84%E8%AE%AD%E7%BB%83%E4%BD%BF%E7%94%A8%E7%9A%84%E6%95%B0%E6%8D%AE%E9%9B%86">预训练使用的数据集</a></li>
<li><a href="#%E9%A2%84%E8%AE%AD%E7%BB%83%E6%97%B6%E7%9A%84%E7%BD%91%E7%BB%9C%E7%BB%93%E6%9E%84">预训练时的网络结构</a></li>
<li><a href="#%E9%A2%84%E8%AE%AD%E7%BB%83%E6%97%B6%E7%9A%84%E8%BE%93%E5%85%A5">预训练时的输入</a></li>
</ul>
</li>
<li><a href="#%E6%AD%A3%E5%BC%8F%E8%AE%AD%E7%BB%83">正式训练</a>
<ul>
<li><a href="#%E8%AE%AD%E7%BB%83%E6%97%B6%E7%9A%84%E7%BD%91%E7%BB%9C%E7%BB%93%E6%9E%84">训练时的网络结构</a></li>
<li><a href="#%E8%AE%AD%E7%BB%83%E6%97%B6%E7%9A%84%E8%BE%93%E5%85%A5">训练时的输入</a></li>
<li><a href="#%E6%9C%80%E5%90%8E%E4%B8%80%E5%B1%82">最后一层</a></li>
</ul>
</li>
<li><a href="#%E6%8D%9F%E5%A4%B1%E5%87%BD%E6%95%B0">损失函数</a></li>
</ul>
</li>
<li><a href="#%E6%A8%A1%E5%9E%8B%E7%9A%84%E7%BC%BA%E7%82%B9">模型的缺点</a></li>
</ul>
<!-- tocstop -->
<h2><span id="什么是yolo">什么是YOLO</span></h2>
<p>YOLO是一个实时目标检测模型，其基本框架是一个single network，属于one-stage范畴。在YOLO之前，很多方法都是用滑动窗口或者region proposals的方法直接把bounding boxes找出来，然后再在这些框里面分别使用分类器，进行目标检测；而YOLO则颠覆了这种做法，将目标检测视为回归问题进行研究。</p>
<p>YOLO后续有相当多的变种，此文聚焦在YOLOV1上，所以之后的YOLO都表示YOLOV1。</p>
<p>通过某种人为规定的方式选框，然后在这些框里利用回归的方法来预测bounding boxes的置信度以及框内物体的种类。而相比之前的模型和方法，YOLO的检测速度大幅提高，检测精度相比RCNN之类有所下降，但和以前的实时检测模型相比，精度确实提升了两倍多；此外YOLO有着优秀的泛化能力。</p>
<h2><span id="预测流程">预测流程</span></h2>
<p><img src="https://gitee.com/tina-yao/bigbig-shark/raw/master/imgs/YOLOV1img/1.png" alt></p>
<p>给定一张固定大小的图片，将其均匀划分成$S\times S$个网格；如果图片中某个物体的中心落在某个网格中，那么就由该网格来负责预测该物体。</p>
<h3><span id="边界框">边界框</span></h3>
<p>而每个网格将预测B个边界框，同时依据框内是否可能存在物体来计算出这些框的置信度（如下图中的中间顶排的图所示）；图中框粗细代表这个框的置信度（confidence）和准确度，越粗说明这个框内存在物体的可能性越大、这个框位置和大小的准确性越好）。</p>
<p><img src="https://gitee.com/tina-yao/bigbig-shark/raw/master/imgs/YOLOV1img/3.png" alt></p>
<h4><span id="如何理解上图中编码行为">如何理解上图中编码行为</span></h4>
<p>一张图有S x S (=7 x 7)个grid，每个grid会被拿去干两件事：(1) 计算边界框，而一个grid有B (=2)个边界框，1个边界框有5个属性值；(2) 计算属于某个类别的概率，而数据集一共有C (=20)个类别。所以是S x S x (B x 5 + C) Tensor。</p>
<h4><span id="边界框的置信度">边界框的置信度</span></h4>
<p>包括两个评价标准：</p>
<ol>
<li>该框内包含物体的可能性大小；</li>
<li>该框位置的准确性。</li>
</ol>
<p>其计算方式如下：<img src="https://gitee.com/tina-yao/bigbig-shark/raw/master/imgs/YOLOV1img/11.png" alt></p>
<p>前一项表示该框包括物体的可能性，后一项表示预测出来的边界框和真实边界框的交并比（IoU），两者相乘，就是预测框最终的置信度。显然，当框内没有物体时，置信度直接就为0了。</p>
<h4><span id="边界框的任务">边界框的任务</span></h4>
<p>每个边界框都需要预测5个量：x, y, w, h, and confidence，其中：(x, y)是框的中心位置坐标，w和h是预测框的宽度和高度，confidence即上述所说的边界框的位置置信度，用真实边界框和预测边界框的IoU来衡量。</p>
<h3><span id="预测类别的条件概率">预测类别的条件概率</span></h3>
<p>每个网格除了预测两个边界框，还要预测该网格内物体属于某一类物体的概率，因为其前提是该网格内有物体存在，所以具体该概率是条件概率，表示如下：</p>
<p><img src="https://gitee.com/tina-yao/bigbig-shark/raw/master/imgs/YOLOV1img/12.png" alt></p>
<p>具体来说，该式的含义是，在该网格中确实含有物体的前提下，属于第i类物体的概率大小。</p>
<h3><span id="class-specific-confidence-scores-for-each-box">Class-specific confidence scores for each box</span></h3>
<p>（<s>这个词组用中文我不知道怎么说，简洁一点就不够全面，全面还不如直接不翻译。</s>）</p>
<p><img src="https://gitee.com/tina-yao/bigbig-shark/raw/master/imgs/YOLOV1img/2.png" alt></p>
<h2><span id="网络结构">网络结构</span></h2>
<ul>
<li>
<p>24层卷积 + 2层全连接，前者用来提取特征，后者用来预测边界框的四个坐标、物体类别及对应概率。</p>
</li>
<li>
<p>交替使用1 x 1 conv来降维</p>
</li>
<li>
<p>预测时输入图片的分辨率为448 x 448</p>
</li>
<li>
<p>在ImageNet的Classification数据集上进行了预训练；预训练时输入图片分辨率为224 x 224</p>
<p><img src="https://gitee.com/tina-yao/bigbig-shark/raw/master/imgs/YOLOV1img/4.png" alt></p>
</li>
</ul>
<h2><span id="fast-yolo">Fast YOLO</span></h2>
<p>本文通过Fast YOLO来进一步扩展实时目标检测中<strong>实时</strong>的边界。不同于YOLOV1共24层的卷积，Fast YOLO只有9层卷积。其他的都一样。</p>
<h2><span id="训练">训练</span></h2>
<h3><span id="预训练">预训练</span></h3>
<h4><span id="预训练使用的数据集">预训练使用的数据集</span></h4>
<p>ImageNet 1000-class competition dataset</p>
<h4><span id="预训练时的网络结构">预训练时的网络结构</span></h4>
<p><img src="https://gitee.com/tina-yao/bigbig-shark/raw/master/imgs/YOLOV1img/13.png" alt></p>
<h4><span id="预训练时的输入">预训练时的输入</span></h4>
<p>224 x 224</p>
<h3><span id="正式训练">正式训练</span></h3>
<h4><span id="训练时的网络结构">训练时的网络结构</span></h4>
<p>训练时的网络结构如之前“网络结构”模块所描述。</p>
<blockquote>
<p>有趣的是作者这样说道：别人的实验表明同时加上卷积层和全连接层到预训练好的网络上可以提升模型表现，所以我们又加了4个卷积层和2个全连接层。</p>
<p>这不禁令人想到，作者团队是先想到预训练模型，还是先想到的训练模型？</p>
<p>当然这不重要，重要的是，数据集不一样，预训练后在后面加一些层进行微调是合理的。</p>
<p>（如果直接加2个全连接层，而不加那4个卷积层，效果如何？）</p>
</blockquote>
<h4><span id="训练时的输入">训练时的输入</span></h4>
<p>因为检测需要实现细粒度上的检测，所以将输入的分辨率改成了448 x 448。</p>
<h4><span id="最后一层">最后一层</span></h4>
<p>预测出类别概率和边界框的坐标，最后还要对四个坐标（注意是中心坐标、宽、高）进行标准化。</p>
<h3><span id="损失函数">损失函数</span></h3>
<p><img src="https://gitee.com/tina-yao/bigbig-shark/raw/master/imgs/YOLOV1img/10.png" alt></p>
<p>（注明：此图非博主原创，来自B站up同济子豪兄）</p>
<ul>
<li>ij：第i个网格第j个预测框</li>
</ul>
<h2><span id="模型的缺点">模型的缺点</span></h2>
<ul>
<li>人为地去分格子且每个格子预测框数量有限</li>
<li>对于集群的小物体，识别能力有限</li>
<li>泛化能力比较差</li>
<li>比较多的错误定位，模型预测错误的主要原因是不正确的定位。</li>
</ul>

      
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